data-analysis

name

data-analysis

description

Use this skill for advanced data manipulation, statistical analysis, and visualization. Use Python with pandas, numpy, and matplotlib/seaborn to perform tasks like: deep data cleaning, trend analysis, statistical testing, creating charts/plots, and generating insights from structured data (CSV, JSON, SQL, etc.). If the user wants to understand patterns, see graphs, or perform complex calculations on data, use this skill. IMPORTANT: After analyzing data with this skill, use the 'frontend-design' skill directly to generate interactive HTML dashboards for presenting results visually. NEVER write Python to generate HTML.

Data Analysis in EverFern

MANDATORY WORKFLOW

Step 1: First, detect the data file type and use the appropriate skill to read and understand the data structure:

• For .csv files: use the csv skill
• For .xlsx files: use the xlsx skill
• For .pdf files: use the pdf skill
• For .json files: use the json skill
• For .docx files: use the docx skill
• For plain text: use the txt skill

Step 2: Then use this data-analysis skill to:

• Perform statistical analysis
• Clean and transform data
• Generate visualizations

Step 3: Finally, ALWAYS use the 'frontend-design' and 'charts' skills to:

• Generate an interactive HTML dashboard directly
• Present results using ApexCharts (as defined in the charts skill)
• Create charts that the user can interact with

This 3-step workflow is MANDATORY for any data visualization task.

Overview

EverFern leverages Python for high-performance data analysis. This allows for complex operations that exceed simple text-based processing.

Key Libraries

• pandas: Primary tool for tabular data manipulation (DataFrames).
• numpy: Numerical calculations and array operations.
• matplotlib/seaborn: Data visualization and plotting.

Common Workflows

1. Data Loading and Cleaning

import pandas as pd
import numpy as np

# Load data (use absolute R-string paths on Windows)
df = pd.read_csv(r'C:\Users\Username\Downloads\data.csv')

# Handle missing values
df['category'] = df['category'].fillna('Unknown')
df['value'] = df['value'].interpolate()

# Remove outliers
q_low = df["value"].quantile(0.01)
q_hi  = df["value"].quantile(0.99)
df_filtered = df[(df["value"] < q_hi) & (df["value"] > q_low)]

2. Exploratory Data Analysis (EDA)

# Statistical summaries
summary = df.describe()

# Correlation matrix
corr = df.select_dtypes(include=[np.number]).corr()

# Grouping and aggregation
monthly_stats = df.groupby(pd.Grouper(key='date', freq='M')).agg({
    'sales': ['sum', 'mean', 'count'],
    'customer_id': 'nunique'
})

3. Data Visualization

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10, 6))
sns.set_style("whitegrid")

# Create a trend line
sns.lineplot(data=df, x='date', y='value', hue='category')

plt.title('Value Trend Over Time by Category')
plt.xlabel('Date')
plt.ylabel('Value')
plt.xticks(rotation=45)

# Save the plot for the user to see (EverFern can display images saved to specific locations)
plt.savefig(r'C:\Users\Username\Downloads\analysis_plot.png', dpi=300, bbox_inches='tight')
plt.show()

Best Practices

• Reliability: Always check for None or NaN values before performing calculations.
• Performance: Use vectorised pandas operations instead of iterating over rows with for loops.
• Privacy: If handling sensitive files, ensure absolute paths are handled carefully and not leaked in thought blocks if not necessary.
• Visuals: When creating plots, use high-DPI (300) settings for professional results.
• Windows Paths: Always use absolute paths with r'...' (raw strings) to avoid escape character issues.
• Windows Python: ALWAYS use python — NEVER python3. The command python3 does not exist on Windows.